In recent years, deep neural networks have played a particularly critical role in the face of many computer vision tasks, such as image classification [1]}, object detection [2]}, semantic segmentation [3]} and so on. However, training a perfect neural network demands a large amount of data and corresponding data labeling, which is very time-consuming and expensive. When faceing a new task or a new dataset, the previously trained model may exhibit poor performance due to the domain shift. We hope to use available network and data to complete the target task through knowledge transfer, where domain adaptation methods are needed. The problem that domain adaptation figures out is how to adapt the model trained in the source domain with rich labels to the target domain with sparse labels, and minimize the negative transfer. Besides, UDA means that we only have annotations on the source domain data, without the target domain.
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